Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Background: The purpose of our study was to analyze the effects of hospital volume and region on in-hospital and long-term mortality, direct medical costs (DMCs), and length of hospital stay (LOS) in elderly patients following hip fracture, utilizing nationwide claims data. Methods: This retrospective nationwide study sourced its subjects from the Korean National Health Insurance Review and Assessment Service database spanning from January 2011 to December 2018. A generalized estimating equation model with a Poisson distribution and logarithmic link function was used to estimate adjusted odds ratios (aORs) and 95% CIs to assess the association of hospital volume with in-hospital and 1-year mortality, DMCs, and LOS . Results: A total of 172,144 patients were included. Comparing the risk of in-hospital death between high-volume and low-volume hospitals, the risk of in-hospital death was 1.2 times higher at low-volume hospitals (aOR, 1.20; 95% CI, 1.07–1.33; p = 0.002).Additionally, the risk of death at 1 year was 1.05 times higher at low-volume hospitals (aOR, 1.05; 95% CI, 1.01–1.09; p = 0.008) compared to high-volume hospitals. DMCs were 0.84 times lower at low-volume hospitals for in-hospital period (aOR, 0.84; 95% CI, 0.84–0.85; p < 0.001) and 0.87 times lower for 1 year (aOR, 0.87; 95% CI, 0.86–0.88; p < 0.001) compared to high-volume hospitals. In-hospital LOS was 1.21 times longer at low-volume hospitals (aOR, 1.21; 95% CI, 1.20–1.22; p < 0.001) than at high-volume hospitals. In addition, the risk of in-hospital death was 1.22 times higher (aOR, 1.22; 95% CI, 1.12–1.33; p < 0.001) and the risk of 1-year death was 1.07 times higher (aOR, 1.07; 95% CI, 1.04–1.10; p < 0.001) at rural hospitals compared to urban hospitals. Conclusions Clinicians should focus on improving clinical outcomes for hip fracture patients in low-volume and rural hospital settings, with a specific emphasis on reducing mortality rates.

Objective: Several miRNAs have been identified as differentially expressed in patients with poor ovarian response (POR) compared to those with normal responses. This study aims to assess the potential of serum miR-329-3p as a biomarker for diagnosing POR. Methods: We conducted a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to confirm the target genes of miR-329-3p. KGN cells were transfected with both miR-329-3p mimic and inhibitor to assess the differential expression of these target genes. In accordance with the Bologna criteria, we enrolled 16 control patients and 16 patients with POR. We collected patient samples, including serum from day 2 and the human chorionic gonadotropin (hCG) day, as well as granulosa and cumulus cells, to validate the expression of miR-329-3p using quantitative real-time polymerase chain reaction. Results: KEGG pathway analysis revealed that miR-329-3p targeted adenylyl cyclase 9 (ADCY9) and protein kinase A subunit beta (PRKACB), both of which are involved in ovarian steroidogenesis. In KGN cells treated with a miR-329-3p mimic, ADCY9 and PRKACB expression levels were significantly reduced (p<0.05). Elevated levels of miR-329-3p suppressed aromatase expression and 17β-estradiol production by modulating ADCY9 and PRKACB in KGN cells. These effects were also observed in POR patients. Follicle-stimulating hormone receptor (FSHR) expression was diminished in the granulosa cells of POR patients. On day 2, on hCG day, and in granulosa cells, miR-329-3p exhibited high expression levels in the serum of POR patients. Conclusion miR-329-3p exhibited increased expression in granulosa cells and in the sera of POR patients. Consequently, we propose that miR-329-3p may be a potential biomarker for the diagnosis of POR.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Background: The purpose of our study was to analyze the effects of hospital volume and region on in-hospital and long-term mortality, direct medical costs (DMCs), and length of hospital stay (LOS) in elderly patients following hip fracture, utilizing nationwide claims data. Methods: This retrospective nationwide study sourced its subjects from the Korean National Health Insurance Review and Assessment Service database spanning from January 2011 to December 2018. A generalized estimating equation model with a Poisson distribution and logarithmic link function was used to estimate adjusted odds ratios (aORs) and 95% CIs to assess the association of hospital volume with in-hospital and 1-year mortality, DMCs, and LOS . Results: A total of 172,144 patients were included. Comparing the risk of in-hospital death between high-volume and low-volume hospitals, the risk of in-hospital death was 1.2 times higher at low-volume hospitals (aOR, 1.20; 95% CI, 1.07–1.33; p = 0.002).Additionally, the risk of death at 1 year was 1.05 times higher at low-volume hospitals (aOR, 1.05; 95% CI, 1.01–1.09; p = 0.008) compared to high-volume hospitals. DMCs were 0.84 times lower at low-volume hospitals for in-hospital period (aOR, 0.84; 95% CI, 0.84–0.85; p < 0.001) and 0.87 times lower for 1 year (aOR, 0.87; 95% CI, 0.86–0.88; p < 0.001) compared to high-volume hospitals. In-hospital LOS was 1.21 times longer at low-volume hospitals (aOR, 1.21; 95% CI, 1.20–1.22; p < 0.001) than at high-volume hospitals. In addition, the risk of in-hospital death was 1.22 times higher (aOR, 1.22; 95% CI, 1.12–1.33; p < 0.001) and the risk of 1-year death was 1.07 times higher (aOR, 1.07; 95% CI, 1.04–1.10; p < 0.001) at rural hospitals compared to urban hospitals. Conclusions Clinicians should focus on improving clinical outcomes for hip fracture patients in low-volume and rural hospital settings, with a specific emphasis on reducing mortality rates.

Objective: Several miRNAs have been identified as differentially expressed in patients with poor ovarian response (POR) compared to those with normal responses. This study aims to assess the potential of serum miR-329-3p as a biomarker for diagnosing POR. Methods: We conducted a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to confirm the target genes of miR-329-3p. KGN cells were transfected with both miR-329-3p mimic and inhibitor to assess the differential expression of these target genes. In accordance with the Bologna criteria, we enrolled 16 control patients and 16 patients with POR. We collected patient samples, including serum from day 2 and the human chorionic gonadotropin (hCG) day, as well as granulosa and cumulus cells, to validate the expression of miR-329-3p using quantitative real-time polymerase chain reaction. Results: KEGG pathway analysis revealed that miR-329-3p targeted adenylyl cyclase 9 (ADCY9) and protein kinase A subunit beta (PRKACB), both of which are involved in ovarian steroidogenesis. In KGN cells treated with a miR-329-3p mimic, ADCY9 and PRKACB expression levels were significantly reduced (p<0.05). Elevated levels of miR-329-3p suppressed aromatase expression and 17β-estradiol production by modulating ADCY9 and PRKACB in KGN cells. These effects were also observed in POR patients. Follicle-stimulating hormone receptor (FSHR) expression was diminished in the granulosa cells of POR patients. On day 2, on hCG day, and in granulosa cells, miR-329-3p exhibited high expression levels in the serum of POR patients. Conclusion miR-329-3p exhibited increased expression in granulosa cells and in the sera of POR patients. Consequently, we propose that miR-329-3p may be a potential biomarker for the diagnosis of POR.

Background: The purpose of our study was to analyze the effects of hospital volume and region on in-hospital and long-term mortality, direct medical costs (DMCs), and length of hospital stay (LOS) in elderly patients following hip fracture, utilizing nationwide claims data. Methods: This retrospective nationwide study sourced its subjects from the Korean National Health Insurance Review and Assessment Service database spanning from January 2011 to December 2018. A generalized estimating equation model with a Poisson distribution and logarithmic link function was used to estimate adjusted odds ratios (aORs) and 95% CIs to assess the association of hospital volume with in-hospital and 1-year mortality, DMCs, and LOS . Results: A total of 172,144 patients were included. Comparing the risk of in-hospital death between high-volume and low-volume hospitals, the risk of in-hospital death was 1.2 times higher at low-volume hospitals (aOR, 1.20; 95% CI, 1.07–1.33; p = 0.002).Additionally, the risk of death at 1 year was 1.05 times higher at low-volume hospitals (aOR, 1.05; 95% CI, 1.01–1.09; p = 0.008) compared to high-volume hospitals. DMCs were 0.84 times lower at low-volume hospitals for in-hospital period (aOR, 0.84; 95% CI, 0.84–0.85; p < 0.001) and 0.87 times lower for 1 year (aOR, 0.87; 95% CI, 0.86–0.88; p < 0.001) compared to high-volume hospitals. In-hospital LOS was 1.21 times longer at low-volume hospitals (aOR, 1.21; 95% CI, 1.20–1.22; p < 0.001) than at high-volume hospitals. In addition, the risk of in-hospital death was 1.22 times higher (aOR, 1.22; 95% CI, 1.12–1.33; p < 0.001) and the risk of 1-year death was 1.07 times higher (aOR, 1.07; 95% CI, 1.04–1.10; p < 0.001) at rural hospitals compared to urban hospitals. Conclusions Clinicians should focus on improving clinical outcomes for hip fracture patients in low-volume and rural hospital settings, with a specific emphasis on reducing mortality rates.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: Several miRNAs have been identified as differentially expressed in patients with poor ovarian response (POR) compared to those with normal responses. This study aims to assess the potential of serum miR-329-3p as a biomarker for diagnosing POR. Methods: We conducted a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to confirm the target genes of miR-329-3p. KGN cells were transfected with both miR-329-3p mimic and inhibitor to assess the differential expression of these target genes. In accordance with the Bologna criteria, we enrolled 16 control patients and 16 patients with POR. We collected patient samples, including serum from day 2 and the human chorionic gonadotropin (hCG) day, as well as granulosa and cumulus cells, to validate the expression of miR-329-3p using quantitative real-time polymerase chain reaction. Results: KEGG pathway analysis revealed that miR-329-3p targeted adenylyl cyclase 9 (ADCY9) and protein kinase A subunit beta (PRKACB), both of which are involved in ovarian steroidogenesis. In KGN cells treated with a miR-329-3p mimic, ADCY9 and PRKACB expression levels were significantly reduced (p<0.05). Elevated levels of miR-329-3p suppressed aromatase expression and 17β-estradiol production by modulating ADCY9 and PRKACB in KGN cells. These effects were also observed in POR patients. Follicle-stimulating hormone receptor (FSHR) expression was diminished in the granulosa cells of POR patients. On day 2, on hCG day, and in granulosa cells, miR-329-3p exhibited high expression levels in the serum of POR patients. Conclusion miR-329-3p exhibited increased expression in granulosa cells and in the sera of POR patients. Consequently, we propose that miR-329-3p may be a potential biomarker for the diagnosis of POR.